Example of Creating Transformer Model Using PyTorch

The following article shows an example of Creating Transformer Model Using PyTorch.

Implementation of Transformer Model Using PyTorch

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import TransformerEncoder, TransformerEncoderLayer

class TransformerModel(nn.Module):
    def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):
        super().__init__()
        self.model_type = 'Transformer'
        self.src_mask = None
        self.pos_encoder = PositionalEncoding(ninp, dropout)
        encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)
        self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
        self.encoder = nn.Embedding(ntoken, ninp)
        self.ninp = ninp
        self.decoder = nn.Linear(ninp, ntoken)

        self.init_weights()

    def generate_square_subsequent_mask(self, sz):
        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
        return mask

    def init_weights(self):
        initrange = 0.1
        self.encoder.weight.data.uniform_(-initrange, initrange)
        self.decoder.bias.data.zero_()
        self.decoder.weight.data.uniform_(-initrange, initrange)

    def forward(self, src):
        if self.src_mask is None or self.src_mask.size(0) != len(src):
            device = src.device
            mask = self.generate_square_subsequent_mask(len(src)).to(device)
            self.src_mask = mask

        src = self.encoder(src) * math.sqrt(self.ninp)
        src = self.pos_encoder(src)
        output = self.transformer_encoder(src, self.src_mask)
        output = self.decoder(output)
        return output

In this example, we define a TransformerModel class that inherits from the nn.Module class in PyTorch. The TransformerModel takes in several parameters, such as ntoken (the size of the vocabulary), ninp (the dimensionality of the input embeddings), nhead (the number of attention heads), nhid (the dimensionality of the hidden layer), and nlayers (the number of encoder layers in the Transformer model).

In the constructor of the class, we initialize the various components of the Transformer model, such as the encoder and decoder layers, the positional encoding layer, and the Transformer encoder layer. We also define a method generate_square_subsequent_mask to create the mask used for masking out future positions in the self-attention mechanism.

In the forward method, we first pass the input sequence through the encoder to obtain the input embeddings. We then pass the embeddings through the positional encoding layer and the Transformer encoder layer to obtain the output embeddings. Finally, we pass the output embeddings through the decoder layer to obtain the final output.

This is just a basic example, but you can modify this code to suit your specific use case. You can also experiment with different hyperparameters and architectures to improve the performance of your Transformer model.

Example of Creating Transformer Model Using PyTorch

Implementation of Transformer Model Using PyTorch

Further Reading

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Implementation of Transformer Model Using PyTorch

Further Reading

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